CN114862097B - User side market type identification method based on double-layer evolution game - Google Patents
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Abstract
The invention discloses a user side market type identification method based on double-layer evolution game, which comprises the steps of acquiring data, and establishing a payment function and an operation constraint model of a user side market participant; the acquired data is used as parameters to be imported into an optimization model; establishing a model of a double-layer evolution game, analyzing evolution processes of three user side market types, respectively modeling the three market types as three strategies of market participants in an upper model, modeling a transaction scheme of an energy producer and an energy consumer as an evolution strategy in a lower model, and implanting the two strategies into the problem of the asymmetric evolution game; by integrating the market participant model and the asymmetric evolution game process, the stability strategy of the double-layer evolution game can be modeled and solved. The method of the invention optimizes and identifies the types of the energy market at the user side, fully considers the transaction characteristics of three market types, has comprehensiveness and practicability, and is easy to popularize.
Description
Technical Field
The invention relates to a method for identifying a user side market type, in particular to a method for identifying a user side market type based on double-layer evolution game.
Background
In recent years, with the development of renewable energy generation, internet of things communication technology, and user-level control infrastructure, passive users in conventional unidirectional power systems have become active users (energy producers) capable of controlling their flexible load resources and distributed generation power. In addition to the fact that energy producers and consumers in the current electric power market can sell electricity to the electric network directly and can participate in the virtual power plant project to be uniformly scheduled by the virtual power plant manager, the energy producers and consumers can participate in the point-to-point trading market directly, and accordingly the income of the consumers is maximized. The three types of user-side markets are each characterized in that it is difficult for energy producers and consumers to determine which market should be engaged in to maximize their benefits.
Disclosure of Invention
The invention aims to: the invention provides a user side market type identification method based on double-layer evolution game, which can efficiently and accurately identify the type of a user side market to maximize the income of market participants.
The technical scheme is as follows: in order to achieve the purpose of the invention, the user side market type identification method based on double-layer evolution game comprises the following steps:
a user side market type identification method based on double-layer evolution game is characterized by comprising the following steps:
(1) Acquiring user load, real-time electricity price, manager investment ratio, payment function of market participants and user energy demand;
(2) Establishing a payment function and an operation constraint model of a user-side market participant, and transmitting the data collected in the step (1) into the model as parameters;
(3) Establishing a model of a double-layer evolution game to analyze the evolution process of three user side market types, respectively modeling the three market types as three strategies of market participants in an upper model, modeling a transaction scheme of an energy producer and an energy consumer as an evolution strategy in a lower model, and implanting the two strategies into the problem of the asymmetric evolution game;
(4) Modeling a stability strategy of the double-layer evolution game by considering a market participant model and an asymmetric evolution game process;
(5) Dividing the original model into an upper layer and a lower layer, solving the market type selection by the upper layer, solving the power strategy under the corresponding market type by the lower layer, inputting the optimal transaction result obtained by the lower layer into the upper layer for repeated iterative computation until the selection probability of a certain market is 1, and obtaining a stable solution of the evolution game.
As a further aspect of the present invention, in step (1), the user load data includes load data of the user throughout the year, and a data acquisition interval is 15 minutes at minimum; the real-time electricity price adopts the national unified peak Gu Ping three-time electricity price, and the trading electricity price of the point-to-point market is agreed by the buyer and the seller; the manager investment ratio means that the market at the user side is built by an energy producer and a manager together, the distributed power generation resource is invested by the energy producer and the manager together, and certain ratio exists between the two parties;
the market participant's payment function includes the investment maintenance cost of each participant minus the revenue of participating in the market transaction, and the user's energy demand is its particular energy efficiency function demand, which can be modeled as an energy efficiency value greater than a particular value.
As a further aspect of the present invention, in step (2), the payment function of the user-side market participant and the operation constraint model thereof specifically include:
(21) The participants in the user side market can mainly participate in three markets of VPP, P2P and P2G, so that the payment functions of the market participants in the three markets need to be analyzed, the participants in each market mainly comprise three parties of an aggregator, an energy producer and a power grid company, and the payment functions of the three party bodies in the three markets are as follows:
for the energy producer and the consumer:
for distributed resource aggregators:
For grid companies:
Wherein: c inv is the total investment cost of a distributed power generation source, lambda VPP,λP2G is the income ratio of a system operator and an aggregator in two market modes of virtual power plant and power generation surfing, the income ratio is related to the investment of the system operator and the aggregator, beta VPP,βP2G is the investment ratio of the system operator and the aggregator in two market modes of virtual power plant and power generation surfing, C om is the investment operation cost of the distributed power source, C gas is the cost of a micro gas turbine, C ope,Closs and C FiT are market operation, energy loss and power generation surfing income, I sub,Icut is the electricity selling income and load reduction income, I grid,Idef is the electricity selling income and the investment delaying income of the power grid of the distributed power source, and I VPP and I P2G respectively represent the electricity selling income of the market operator in the two market modes of the virtual power plant and the power generation surfing;
(22) The cost and benefit in each payment function can be modeled specifically as follows:
wherein: lambda is the income ratio of the system operators and the aggregators in the user side market, beta is the ratio of the investment of the system operators and the aggregators in the user side market in the total investment, and the investment cost C inv considers the photovoltaic investment Investment/>, with distributed energy storage devicesIn three market modes, N pv is the installation number of photovoltaic devices, N bess is the installation number of energy storage devices, p pv is the rated power of the photovoltaic devices, p bess is the rated power of the energy storage devices, U pv is the investment cost per unit capacity of photovoltaic, U bess is the investment cost per unit capacity of energy storage, R is the investment discount rate of distributed resources, R B is the number of times of energy storage substitution, L pv and L bess are the lives of photovoltaic and energy storage respectively, M pv,Mbess is the cost per unit photovoltaic and energy storage capacity respectively, and the two investment costs directly affect the operation and maintenance costs of the photovoltaic and the energy storage respectively,/>For the rated power of the gas turbine, M gas is the heat value of natural gas, eta p is the power generation efficiency of the gas turbine, and the point-to-point transaction and the selling benefit of the virtual power plant are obtained by photovoltaic rated power/>Load power/>Virtual Power plant trade Power/>And power to power generation network/>And (5) determining. The unit prices of the power generation patches are respectivelyAnd/>Omega i and alpha i are a priori parameters of the user i comfort function,/>, respectivelySince the load aggregator does not directly profit in the point-to-point energy trading market, the invention sets the profit of the aggregator as a constant value, c loss is the unit loss cost of electric energy transmission, p loss is the electric energy lost in transmission, x and y are respectively Boolean variables representing the flow direction of tide,/>, for the load power of user iUnit cost for investment upgrade of the power grid;
(23) During the transaction, each distributed resource has its corresponding physical constraint, so the market run-time
Constraints need to be considered as follows:
wherein: rated power for energy storage at user side,/> For the charge state of the energy storage at the time t,/>For the self-discharge rate of the stored energy, θ cha and θ dis are respectively the charge-discharge efficiency of the stored energy, b l is the admittance of the line l, and the head-end nodes of the line l are respectively i and j. The meaning of the remaining variables is consistent with the variables in (22).
As a further aspect of the present invention, the step (3) specifically includes:
(31) The strategy set of the asymmetric evolution game model comprises three types of transaction schemes in the market of the user side, and the expression can be modeled as follows:
wherein: Transaction strategy sets of power generation surfing, virtual power plant and point-to-point transaction market respectively, wherein the strategy sets of the three market types mainly comprise output power of a distributed power supply, energy storage power and photovoltaic power generation power, which are respectively/>
(32) After defining the strategy set of the evolution of the upper layer and the lower layer, the replicator dynamic state of the evolution game can be modeled as follows:
Wherein: x 1',x2',x3' are the rates of change of the individual proportions of selection strategy S 1,S2,S3, The individual proportion of selection strategy S 1,S2,S3 to the population,/>, respectivelyTo select strategy/>Is a function of the payment of the market participant,The average value of the overall payment function is also represented by the payment functions and average values of the other two strategies.
As a further aspect of the present invention, the step (4) includes:
Considering a market participant model and an asymmetric evolution game process, the stability strategy of the double-layer evolution game can be modeled as follows:
wherein: ζ is the proportionality coefficient of the strategy S i, if all market individuals adopt the strategy S i, the fitness of S i will be higher than all other possible strategies, so that the strategy S i will not generate any mutation, and is an evolution stabilization strategy.
The beneficial effects are that: considering the multiple types of large-scale energy producers and consumers participating in the market at the user side, the method and the device identify the optimal market type at the user side based on the double-layer evolution game model, ensure that the energy demand of the user is met, improve the efficiency of the transaction market at the whole user side, and have stronger use value.
Drawings
FIG. 1 is a schematic diagram of a user-side marketplace according to the present invention;
FIG. 2 is a solution flow chart of a two-layer evolutionary game model proposed by the present invention;
Detailed Description
The technical scheme of the invention is further described below with reference to the accompanying drawings.
Referring to fig. 1, a block diagram of three user side markets related to the present invention is shown, which mainly includes three parts, namely a transaction layer, a scheduling layer and a physical layer, after the transaction of the transaction layer is finished, the transaction result is sent to the scheduler and the physical layer, the physical layer determines the feasibility of the transaction by calculating the line load flow, and the strategy of the adjustment of the transaction scheme is uploaded to the scheduling layer and the transaction layer. The method comprises the following steps:
(1) Acquiring data such as user load, real-time electricity price, manager investment ratio, payment function of market participants, user energy demand and the like, and transmitting the collected data into an optimization model as parameters;
Further, the user load data comprises annual load data of the user, and the data acquisition interval is 15 minutes at minimum;
Further, the real-time electricity price adopts the national unified peak Gu Ping three-time electricity price, and the trading electricity price of the point-to-point market is agreed by buyers and sellers;
Further, the manager investment ratio means that the user side market is built by the energy generator and the manager together, the distributed power generation resource is invested by the energy generator and the manager together, and certain ratio exists between the two;
further, the market participant's payment function includes the investment maintenance cost of each participant minus the revenue of participating in the market transaction;
Further, the user's energy requirement is its specific energy efficiency function requirement, and can be modeled as an energy efficiency value greater than a specific value.
(2) Establishing a payment function and an operation constraint model of a user side market participant;
(21) Participants in the customer side market can participate in three markets, namely VPP, P2P and P2G, and the structure of the three market types and the relationship between the participants are shown in FIG. 2. There is therefore a need to analyze the payment functions of market participants in these three markets, each market participant mainly comprising an aggregator, an energy producer and a grid company, so the payment functions of the three parties under the three markets are as follows:
for the energy producer and the consumer:
for distributed resource aggregators:
For grid companies:
Wherein: c inv is the total investment cost of the distributed power generation source, lambda VPP,λP2G is the income ratio of a system operator and an aggregator in two market modes of virtual power plant and power generation surfing, the income ratio is related to the investment of the system operator and the aggregator, beta VPP,βP2G is the investment ratio of the system operator and the aggregator in two market modes of virtual power plant and power generation surfing, C om is the investment operation cost of the distributed power source, C gas is the cost of a micro gas turbine, C ope,Closs and C FiT are market operation, energy loss and power generation surfing income, I sub,Icut is the electricity selling income and load reduction income, I grid,Idef is the electricity selling income and the investment delaying income of the power grid of the distributed power source, and I VPP and I P2G respectively represent the electricity selling income of the market operator in the two market modes of the virtual power plant and the power generation surfing.
(22) The cost and benefit in each payment function can be modeled specifically as follows:
wherein: lambda is the income ratio of the system operators and the aggregators in the user side market, beta is the ratio of the investment of the system operators and the aggregators in the user side market in the total investment, and the investment cost C inv considers the photovoltaic investment Investment/>, with distributed energy storage devicesIn three market modes, N pv is the installation number of photovoltaic devices, N bess is the installation number of energy storage devices, p pv is the rated power of the photovoltaic devices, p bess is the rated power of the energy storage devices, U pv is the investment cost per unit capacity of photovoltaic, U bess is the investment cost per unit capacity of energy storage, R is the investment discount rate of distributed resources, R B is the number of times of energy storage substitution, L pv and L bess are the lives of photovoltaic and energy storage respectively, M pv,Mbess is the cost per unit photovoltaic and energy storage capacity respectively, and the two investment costs directly affect the operation and maintenance costs of the photovoltaic and the energy storage respectively,/>For the rated power of the gas turbine, M gas is the heat value of natural gas, eta p is the power generation efficiency of the gas turbine, and the point-to-point transaction and the selling benefit of the virtual power plant are obtained by photovoltaic rated power/>Load power/>Virtual Power plant trade Power/>And power to power generation network/>And (5) determining. The unit prices of the power generation patches are respectivelyAnd/>Omega i and alpha i are a priori parameters of the user i comfort function,/>, respectivelySince the load aggregator does not directly profit in the point-to-point energy trading market, the invention sets the profit of the aggregator as a constant value, c loss is the unit loss cost of electric energy transmission, p loss is the electric energy lost in transmission, x and y are respectively Boolean variables representing the flow direction of tide,/>, for the load power of user iAnd the unit cost for the investment upgrade of the power grid.
(23) During the transaction, each distributed resource has its corresponding physical constraint, so the market run-time
Constraints need to be considered as follows:
wherein: rated power for energy storage at user side,/> For the charge state of the energy storage at the time t,/>For the self-discharge rate of the stored energy, θ cha and θ dis are respectively the charge-discharge efficiency of the stored energy, b l is the admittance of the line l, and the head-end nodes of the line l are respectively i and j. The meaning of the remaining variables is consistent with the variables in (22).
(3) In order to further analyze the evolution process of three user side market types, a double-layer evolution game model is established, the three market types are respectively modeled as three strategies of market participants in an upper layer model, the trading scheme of an energy producer and an energy consumer is modeled as an evolution strategy in a lower layer model, and the two strategies are implanted into the problem of the asymmetric evolution game;
(31) The strategy set of the asymmetric evolution game model comprises three types of transaction schemes in the market of the user side, and the expression can be modeled as follows:
wherein: Transaction strategy sets of power generation surfing, virtual power plant and point-to-point transaction market respectively, wherein the strategy sets of the three market types mainly comprise output power of a distributed power supply, energy storage power and photovoltaic power generation power, which are respectively/>
(32) After defining the strategy set of the evolution of the upper layer and the lower layer, the replicator dynamic state of the evolution game can be modeled as follows:
Wherein: x 1',x2',x3' are the rates of change of the individual proportions of selection strategy S 1,S2,S3, The individual proportion of selection strategy S 1,S2,S3 to the population,/>, respectivelyTo select strategy/>Is a function of the payment of the market participant,The average value of the overall payment function is also represented by the payment functions and average values of the other two strategies.
(4) Integrating the market participant model and the asymmetric evolution game process, the stability strategy of the double-layer evolution game can be modeled as follows:
wherein: ζ is the scaling factor of strategy S i. If all market individuals adopted the strategy S i, the adaptability of S i is higher than all other possible strategies, so that the strategy S i does not generate any mutation and is an evolution stabilization strategy.
(5) The asymmetric evolution game is divided into two layers because market participants need to confirm the type of the market at the user side before obtaining the optimal transaction result. The upper layer determines the market structure as a virtual power plant, power generation, internet surfing, or point-to-point transaction. The lower layer dynamically calculates corresponding evolution stability points according to replicators, and returns evolution results to the upper layer, and a solving flow chart of the double-layer evolution game is shown in figure 2. It should be noted that the upper policy set is a market set. After the trade form is determined, each person at the lower layer randomly selects the trade power of the person, and in order to calculate the developed stable solution, the trade power needs to be repeatedly selected until all three market types are selected. By calculating the fitness of the evolution strategy, the invention can model replicators dynamically, while the stable solution of the whole evolution can be obtained by repeating the iteration of the upper and lower layers until the maximum iteration time limit is reached.
The method is suitable for type identification of the user side electric power transaction market, analyzes the cost and income of the energy generator and the consumer to participate in the market from the perspective of market participants, optimizes and analyzes the evolution stability strategies of three market types, provides a new thought for identifying the optimal demand side market type for the user, and effectively promotes the application of various user side markets in the current electric power market.
Claims (2)
1. A user side market type identification method based on double-layer evolution game is characterized by comprising the following steps:
(1) Acquiring user load, real-time electricity price, manager investment ratio, payment function of market participants and user energy demand;
(2) Establishing a payment function and an operation constraint model of a user-side market participant, and transmitting the data collected in the step (1) into the model as parameters;
(3) Establishing a model of a double-layer evolution game to analyze the evolution process of three user side market types, respectively modeling the three market types as three strategies of market participants in an upper model, modeling a transaction scheme of an energy producer and an energy consumer as an evolution strategy in a lower model, and implanting the two strategies into the problem of the asymmetric evolution game;
(4) Modeling a stability strategy of the double-layer evolution game by considering a market participant model and an asymmetric evolution game process;
(5) Dividing an original model into an upper layer and a lower layer, solving a market type selection by the upper layer, solving a power strategy under a corresponding market type by the lower layer, inputting an optimal transaction result obtained by the lower layer into the upper layer for repeated iterative computation until the selection probability of a certain market is 1, and obtaining a stable solution of the evolution game;
in the step (2), the payment function and the operation constraint model of the user side market participant specifically include:
(21) The participants in the user side market mainly participate in three markets of VPP, P2P and P2G, so that the payment functions of the market participants in the three markets need to be analyzed, the participants in each market mainly comprise three parties of an aggregator, an energy producer and a power grid company, and the payment functions of the three party bodies in the three markets are as follows:
for the energy producer and the consumer:
for distributed resource aggregators:
For grid companies:
Wherein: c inv is the total investment cost of a distributed power generation source, lambda VPP,λP2G is the income ratio of a system operator and an aggregator in two market modes of virtual power plant and power generation surfing, the income ratio is related to the investment of the system operator and the aggregator, beta VPP,βP2G is the investment ratio of the system operator and the aggregator in two market modes of virtual power plant and power generation surfing, C om is the investment operation cost of the distributed power source, C gas is the cost of a micro gas turbine, C ope,Closs and C FiT are market operation, energy loss and power generation surfing income, I sub,Icut is the electricity selling income and load reduction income, I grid,Idef is the electricity selling income and the investment delaying income of the power grid of the distributed power source, and I VPP and I P2G respectively represent the electricity selling income of the market operator in the two market modes of the virtual power plant and the power generation surfing;
(22) The cost and benefit in each payment function can be modeled specifically as follows:
wherein: lambda is the income ratio of the system operators and the aggregators in the user side market, beta is the ratio of the investment of the system operators and the aggregators in the user side market in the total investment, and the investment cost C inv considers the photovoltaic investment Investment/>, with distributed energy storage devicesIn three market modes, N pv is the installation number of photovoltaic devices, N bess is the installation number of energy storage devices, p pv is the rated power of the photovoltaic devices, p bess is the rated power of the energy storage devices, U pv is the investment cost per unit capacity of photovoltaic, U bess is the investment cost per unit capacity of energy storage, R is the investment discount rate of distributed resources, R B is the number of times of energy storage substitution, L pv and L bess are the lives of photovoltaic and energy storage respectively, M pv,Mbess is the cost per unit photovoltaic and energy storage capacity respectively, and the two investment costs directly affect the operation and maintenance costs of the photovoltaic and the energy storage respectively,/>For the rated power of the gas turbine, M gas is the heat value of natural gas, eta p is the power generation efficiency of the gas turbine, and the point-to-point transaction and the selling benefit of the virtual power plant are obtained by photovoltaic rated power/>Load powerVirtual Power plant trade Power/>And power to power generation network/>Determining that the unit prices of the power generation patches are respectivelyAnd/>Omega i and alpha i are a priori parameters of the user i comfort function,/>, respectivelySince the load aggregator does not directly profit in the point-to-point energy trading market, the invention sets the profit of the aggregator as a constant value, c loss is the unit loss cost of electric energy transmission, p loss is the electric energy lost in transmission, x and y are respectively Boolean variables representing the flow direction of tide,/>, for the load power of user iUnit cost for investment upgrade of the power grid;
(23) In the transaction process, each distributed resource has corresponding physical constraint, so the constraint needs to be considered when the market runs as follows:
wherein: rated power for energy storage at user side,/> For the charge state of the energy storage at the time t,/>For the self-discharge rate of energy storage, theta cha and theta dis are respectively the charge-discharge efficiency of energy storage, b l is the admittance of a line l, the head and tail end nodes of the line l are i and j respectively, and the meanings of the rest variables are consistent with those of the variables in (22);
the step (3) specifically comprises:
(31) The strategy set of the asymmetric evolution game model comprises three types of transaction schemes in the market of the user side, and the expression can be modeled as follows:
wherein: Transaction strategy sets of power generation surfing, virtual power plant and point-to-point transaction market respectively, wherein the strategy sets of the three market types mainly comprise output power of a distributed power supply, energy storage power and photovoltaic power generation power, which are respectively/>
(32) After defining the strategy set of the evolution of the upper layer and the lower layer, the replicator dynamic state of the evolution game can be modeled as follows:
Wherein: x 1',x2',x3' are the rates of change of the individual proportions of selection strategy S 1,S2,S3, The individual proportion of selection strategy S 1,S2,S3 to the population,/>, respectivelyTo select strategy/>Payment function of market participant,/>The average value of the overall payment function is also represented by the payment functions and the average value of the other two strategies;
the step (4) comprises:
Considering a market participant model and an asymmetric evolution game process, the stability strategy of the double-layer evolution game can be modeled as follows:
wherein: ζ is the proportionality coefficient of the strategy S i, if all market individuals adopt the strategy S i, the fitness of S i will be higher than all other possible strategies, so that the strategy S i will not generate any mutation, and is an evolution stabilization strategy.
2. The method for identifying a user-side market type based on a two-layer evolutionary game according to claim 1, wherein in the step (1), the user load data comprises annual load data of the user, and the data acquisition interval is 15 minutes at minimum; the real-time electricity price adopts the national unified peak Gu Ping three-time electricity price, and the trading electricity price of the point-to-point market is agreed by the buyer and the seller; the manager investment ratio means that the market at the user side is built by an energy producer and a manager together, the distributed power generation resource is invested by the energy producer and the manager together, and certain ratio exists between the two parties;
The market participant's payment function includes the investment maintenance cost of each participant minus the revenue of participating in the market transaction, the user's energy demand being its particular energy efficiency function demand, modeled as an energy efficiency value greater than a particular value.
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